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检索条件"主题词=Evolutionary algorithm"

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Social Adaptation of Robots for Modulating Self-Organization in Animal Societies 8

Social Adaptation of Robots for Modulating Self-Organization...

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2014 IEEE Eighth International Conference on Self Adaptive and Self Organizing Systems Workshops (SASOW 2014)

作者： Zahadat, Payam Bodi, Michael Salem, Ziad Bonnet, Frank de Oliveira, Marcelo Elias Mondada, Francesco Griparic, Karlo Haus, Tomislav Bogdan, Stjepan Mills, Rob Mariano, Pedro Correia, Luis Kernbach, Olga Kernbach, Serge Schmickl, Thomas Karl Franzens Univ Graz Artificial Life Lab Dept Zool Graz Austria Aleppo Univ Dept Comp Engn Elect & Elect Engn Fac Aleppo Syria Swiss Fed Inst Technol EPFL Robot Syst Lab Lausanne Switzerland Fac Elect Engn & Comp Lab Robot & Intelligent Control Syst Zagreb Croatia Univ Lisbon Fac Ciencias LabMAg Lisbon Portugal Cybertronica Res Res Ctr Adv Robot & Environm Sci Stuttgart Germany

ISBN: (纸本)9781479963782

The goal of the work presented here is to influence the overall behaviour of specific animal societies by integrating computational mechatronic devices (robots) into those societies. To do so, these devices should be accepted by the animals as part of the society and/or as part of the collectively formed environment. For that, we have developed two sets of robotic hardware for integrating into societies of two different animals: zebrafish and young honeybees. We also developed mechanisms to provide feedback from the behaviours of societies for the controllers of the robotic system. Two different computational methods are then used as the controllers of the robots in simulation and successfully adapted by evolutionary algorithms to influence the simulated animals for desired behaviours. Together, these advances in mechatronic hardware, feedback mechanisms, and controller methodology are laying essential foundations to facilitate experiments on modulating self-organised behaviour in mixed animal-robot societies.

关键词： animal society Robot Social Adjustment Societies Controllers Societies Microelectromechanical devices Animals evolutionary algorithm self-organization

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Design of IIR Digital Filter using modified chaotic orthogonal imperialist competitive algorithm

Design of IIR Digital Filter using modified chaotic orthogon...

引用

13th Iranian Conference on Fuzzy Systems (IFSC)

作者： Sharifi, MohammadAli Mojallali, Hamed Islamic Azad Univ Dept Elect Comp & Biomed Engn Qazvin Branch Qazvin Iran Univ Guilan Fac Engn Dept Elect Engn Rasht Iran

ISBN: (纸本)9781479912278;9781479912285

There are two types of digital filters including Infinite Impulse Response (IIR) and Finite Impulse Response (FIR). IIR filters attract more attention as they can decrease the filter order significantly compared to FIR filters. Owing to multi-modal error surface, simple powerful optimization techniques should be utilized in designing IIR digital filters to avoid local minimum. Imperialist competitive algorithm (ICA) is an evolutionary algorithm used in solving optimization problems in recent years. ICA can find global optimum response in a nonlinear searching space. In this paper, performance of chaotic orthogonal imperialist competitive algorithm has been improved through some modifications in it. Then, this modified algorithm has been applied in designing IIR digital filters and their performance has been compared to many evolutionary algorithms.

关键词： IIR Digital Filter Imperialist Competitive algorithm Optimization evolutionary algorithm

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An Experimental Study of Prediction Methods in Robust Optimization Over Time

An Experimental Study of Prediction Methods in Robust Optimi...

引用

IEEE Congress on evolutionary Computation (CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Fox, Matthew Yang, Shengxiang Caraffini, Fabio De Montfort Univ Sch Comp Sci & Informat Leicester Leics England

ISBN: (纸本)9781728169293

Robust Optimization Over Time (ROOT) is a new method of solving Dynamic Optimization Problems in respect to choosing a robust solution, that would last over a number of environment changes, rather than the approach that chooses the optimal solution at every change. ROOT methods currently show that ROOT can be solved by predicting an individual fitness for a number of future environment changes. In this work, a benchmark problem based on the Modified Moving Peaks Benchmark (MMPB) is proposed that includes an attractor heuristic, that guides optima to a determined location in the environment, resulting in a more predictable optimum. We study a number of time series forecasting methods to test different prediction methods of future fitness values in a ROOT method. Four time series regression techniques are considered as the prediction method: Linear and Quadratic Regression, an Autoregressive model, and Support Vector Regression. We find that there is not much difference in choosing a simple Linear Regression to more advanced prediction methods. We also suggest that current benchmark problems that cannot be predicted will deceive the optimizer and ROOT framework as the peaks may move using a random walk. Results show an improvement in comparison with MMPB used in most ROOT studies.

关键词： Particle Swarm Optimization Computational Modelling Metaheuristic Optimization Benchmark Testing evolutionary algorithm

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High-dimensional Function Optimisation by Reinforcement Learning

High-dimensional Function Optimisation by Reinforcement Lear...

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2010 IEEE World Congress on Computational Intelligence

作者： Wu, Q. H. Liao, H. L. Univ Liverpool Dept Elect Engn & Elect Liverpool L69 3GJ Merseyside England

ISBN: (纸本)9781424481262

This paper presents a new algorithm, Function Optimisation by Reinforcement Learning (FORL), to solve large-scale and complex function optimisation problems. FORL undertakes the dimensional search in sequence, in contrast to evolutionary algorithms (EAs) which are based on the population-based search, and has the ability of memory of history incorporated via estimating and updating of the values of states that have been visited, which is different from EAs that aggregate the individuals of a population towards the best selected in a current population. With its capability of searching in sequence and memory of history, FORL reduces the number of function evaluations (FEs). FORL has been evaluated, in comparison with several EAs, including recently improved evolutionary Programming, Genetic algorithms, Particle Swarm Optimisation and other efficient EAs, on 23 benchmark functions, which represent a range of most challenging optimisation problems. The simulation studies show that FORL, using a smaller number of FEs, offers better performance in finding accurate solutions, in particular for high-dimensional multi-modal function optimisation problems.

关键词： complex function evolutionary algorithm evolutionary computation function evaluation function evaluation large scale function learning (artificial intelligence) multimodal function optimisation problem solving problem solving reinforcement learning

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The Overview of Genetic algorithm with Tree Chromosome Structure to Identify Functions 6

The Overview of Genetic Algorithm with Tree Chromosome Struc...

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6th International Conference on Fuzzy Systems and Data Mining (FSDM)

作者： Matayoshi, Mitsukuni Dept Ind & Informat Sci Ginowan Okinawa Japan

ISBN: (纸本)9781643681351;9781643681344

This paper is a collection of previous studies for function identification by simple genetic algorithm (GA) [1] with tree chromosome structure which has been proposed in [2]-[7], and gives the details more than survey paper. This paper also aims to introduce the studies which were written in Japanese. In this paper, there are five main points. First, a tree chromosome structure, which is the core idea of the studies, is introduced. The tree chromosome structure makes GA succeed in function identification called symbolic regression. Second, the proposed GA with tree chromosome structure succeeded in identifying the target functions from the observed data are shown indeed. The target functions are algebraic functions, primary transcendental functions, time series functions including chaos function, and user-defined one-variable functions. Third, to find function represented with some parentheses, a hierarchical tree chromosome structure is introduced. Forth, some local search methods to aim at the improvement for identification success rate and shortening identification time are introduced. In the end of this paper, the proposed tree and hierarchical tree chromosome structure can be adapted for identifying Boolean functions are laid out.

关键词： Genetic algorithm evolutionary algorithm Function Identification Symbolic Regression Local Search Time Series Function Boolean Function

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Using Shuffled Frog Leaping algorithm to View Selection Problem Subject to Dual Constraints 4

Using Shuffled Frog Leaping Algorithm to View Selection Prob...

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4th Annual International Conference on Network and Information Systems for Computers (ICNISC)

作者： Chen, Yu-Si Sun, Lin-Fu Southwest Jiaotong Univ Coll Informat Sci & Technol Chengdu 610031 Peoples R China

ISBN: (纸本)9781538669563

This paper presents a shuffled frog leaping algorithm (SFLA) based solution to solve the View Selection Problem (VSP) subject to dual constraints, which is often used to accelerate data warehouse queries. Since VSP is both discrete and constrained, a greedy-repaired strategy under dual constraints is proposed to handle unfeasible solutions. This proposed solution also profits from a mutation strategy in order to improve the quality of solutions, particularly to avoid being trapped in local optima. Experimental results show that under different constraints combinations, SFLA is able to find a near-optimal feasible solution, with maximum error less than 1%. Comparisons with GA and PSO show that SFLA has better solution quality and faster convergence rate, and also scales with the problem size.

关键词： materialized view view selection shuffled frog leaping evolutionary algorithm

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Application of the Hierarchic Memetic Strategy HMS in Neuroevolution 22nd

Application of the Hierarchic Memetic Strategy HMS in Neuroe...

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22nd Annual International Conference on Computational Science (ICCS)

作者： Sokol, Mateusz Smolka, Maciej AGH Univ Sci & Technol Inst Comp Sci Krakow Poland

ISBN: (纸本)9783031087547;9783031087530

Quite recently some noteworthy papers appeared showing classes of deep neural network (DNN) training tasks where rather simple one-population evolutionary algorithms (EA) found better solutions than gradient-based optimization methods. However, it is well known that simple single-population evolutionary algorithms generally suffer from the problem of getting stuck in local optima. A multi-population adaptive evolutionary strategy called Hierarchic Memetic Strategy (HMS) is designed especially to mitigate this problem. HMS was already shown to outperform single-population EAs in general multi-modal optimization and in inverse problem solving. In this paper we describe an application of HMS to the DNN training tasks where the above-mentioned single-population EA won over gradient methods. Obtained results show that HMS finds better solutions than the EA when using the same time resources, therefore proving the advantage of HMS over not only the EA but in consequence also over gradient methods.

关键词： Neuroevolution Deep neural networks evolutionary algorithm

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Application of Breeder GA to Power System Controller Design

Application of Breeder GA to Power System Controller Design

引用

IEEE Swarm Intelligence Symposium

作者： Phiri, A. Folly, K. A. Univ Cape Town Dept Elect Engn ZA-7700 Rondebosch South Africa

ISBN: (纸本)9781424427048

This paper presents the tuning of power system stabilizer (PSS) parameters using a relatively new evolution algorithm called Breeder Genetic algorithms (BGAs). BGAs are based on the concept of "the survival of the fittest" typical to Genetic algorithms (GAs). The main difference between GAs and BGAs is that the evolution of BGAs' population is based on artificial selection similar to the one used by human breeders. However, unlike GAs, the chromosomes in BGAs are always represented as sequences of real numbers rather than sequences of bits or integers. BGAs are particularly suitable to deal with continuous optimization parameters and are a very powerful and versatile optimization algorithm. The proposed BGA-PSS presented in this paper was tested over a wide range of operating conditions and its performance compared with both the Genetic algorithm based PSS (GA-PSS) and the Conventional PSS (CPSS). Simulation results show that the performance of the BGA-PSS is better than that of the GA-PSS and the CPSS. However, both the BGA-PSS and the GA-PSS outperform the CPSS.

关键词： CPSS Genetic algorithm (GA) Parameter optimization evolutionary algorithm Real number controller design Drill bits Optimization algorithms tuning Operating conditions power systems

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Eager Random Search for Differential Evolution in Continuous Optimization 17th

Eager Random Search for Differential Evolution in Continuous...

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17th Portuguese Conference on Artificial Intelligence (EPIA)

作者： Leon, Miguel Xiong, Ning Malardalen Univ Vasteras Sweden

ISBN: (纸本)9783319234854;9783319234847

This paper proposes a memetic computing algorithm by incorporating Eager Random Search (ERS) into differential evolution (DE) to enhance its search ability. ERS is a local search method that is eager to move to a position that is identified as better than the current one without considering other opportunities. Forsaking optimality of moves in ERS is advantageous to increase the randomness and diversity of search for avoiding premature convergence. Three concrete local search strategies within ERS are introduced and discussed, leading to variants of the proposed memetic DE algorithm. The results of evaluations on a set of benchmark problems have demonstrated that the integration of DE with Eager Random Search can improve the performance of pure DE algorithms while not incurring extra computing expenses.

关键词： evolutionary algorithm Differential evolution Eager Random Search Memetic algorithm Optimization

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A new representation method based on mapping functions for analog circuit automatic design

A new representation method based on mapping functions for a...

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6th International Conference on Advanced Computational Intelligence (ICACI)

作者： Zheng, Yong He, Jingsong Univ Sci & Technol China Dept Elect Sci & Technol Hefei 230026 Peoples R China

ISBN: (纸本)9781467363433

Circuit evolutionary design is a way to use evolutionary algorithms to design circuits automatically. Designing a circuit through this method, we must code the circuit first, representing a circuit by a chromosome. This paper proposes an analog circuit coding method based on mapping functions. We use two real numbers between 0 and 1 to represent a component. Mapped by the predefined functions, the two numbers can represent the component type and its connection nodes, they can also represent the component parameter if necessary. The adjacent two components in the chromosome share one-digit code. The coding method has the following features: the chromosome length is nearly equal to the number of components in the circuit, no matter how many components the circuit has;the syntax of the coding is closed, so it won't produce any illegal circuit;the structure of the circuit represented by this coding method is very rich. At last, we conduct two circuit design experiments using the function coding method proposed in this paper, designing a circuit only contain two-terminal components and a circuit containing three-terminal components. We select a typical circuit-a passive filter only containing Land C and an amplifier containing T as our target circuits. The experiment results show that the proposed coding method is effective for designing circuits.

关键词： mapping functions analog circuits representation method chromosome size CIRCUIT DESIGN Computer-Aided Design Electric circuits syntax evolutionary algorithm

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